Most of the existing decentralized stochastic optimization methods are vulnerable to Byzantine attacks. Take DPSGD as an example. At every iteration, every agent mixes the models received from its neighbors, followed by a stochastic gradient step on the cost function constructed from one local data sample (or a batch of them), to update its local model [1]}. When the Byzantine agents send well-designed faulty values instead of the true models, they are able to lead the regular agents to end up with incorrect results.
